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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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A hybrid approach for text summarization using semantic latent Dirichlet allocation and sentence concept mapping with transformer Gurusamy, Bharathi Mohan; Rengarajan, Prasanna Kumar; Srinivasan, Parthasarathy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6663-6672

Abstract

Automatic text summarization generates a summary that contains sentences reflecting the essential and relevant information of the original documents. Extractive summarization requires semantic understanding, while abstractive summarization requires a better intermediate text representation. This paper proposes a hybrid approach for generating text summaries that combine extractive and abstractive methods. To improve the semantic understanding of the model, we propose two novel extractive methods: semantic latent Dirichlet allocation (semantic LDA) and sentence concept mapping. We then generate an intermediate summary by applying our proposed sentence ranking algorithm over the sentence concept mapping. This intermediate summary is input to a transformer-based abstractive model fine-tuned with a multi-head attention mechanism. Our experimental results demonstrate that the proposed hybrid model generates coherent summaries using the intermediate extractive summary covering semantics. As we increase the concepts and number of words in the summary the rouge scores are improved for precision and F1 scores in our proposed model.
Tiny-YOLO distance measurement and object detection coordination system for the BarelangFC robot Susanto, Susanto; Ricardo Silitonga, Jony Arif; Analia, Riska; Jamzuri, Eko Rudiawan; Pamungkas, Daniel Sutopo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6926-6939

Abstract

A humanoid robot called BarelangFC was designed to take part in the Kontes Robot Indonesia (KRI) competition, in the robot coordination division. In this division, each robot is expected to recognize its opponents and to pass the ball towards a team member to establish coordination between the robots. In order to achieve this team coordination, a fast and accurate system is needed to detect and estimate the other robot’s position in real time. Moreover, each robot has to estimate its team members’ locations based on its camera reading, so that the ball can be passed without error. This research proposes a Tiny-YOLO deep learning method to detect the location of a team member robot and presents a real-time coordination system using a ZED camera. To establish the coordinate system, the distance between the robots was estimated using a trigonometric equation to ensure that the robot was able to pass the ball towards another robot. To verify our method, real-time experiments was carried out using an NVDIA Jetson NX Xavier, and the results showed that the robot could estimate the distance correctly before passing the ball toward another robot.
Development of a modified propagation model of a wireless mobile communication system in a 4G network Olukunle, Akande Akinyinka; Kunle, Akinde Olusola; Joel, Odeyinka Oluwadara; Okikiade, Ilori Abolaji; Olusegun, Adigun Matthew; Adeola, Ajagbe Sunday
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6489-6500

Abstract

Pathloss is a key element that causes signal deterioration in the channel as the signal power reduces inversely with propagation distance, this deterioration experienced by the channel is majorly as a result of reflection, absorption, and scattering of the signal. This study however takes into consideration the radio path loss for precise base station (BS), frequency, and power adjustment prediction evaluated over a frequency of 2.3 GHz. With a distance range between 0.1 and 1.5 km for collection of data on the measured received signal strength (MRSS), five empirical models and a modified model were used to validate the measured data to determine their suitability for pathloss prediction at Federal University of Technology, Owerri (FUTO), Imo state, Nigeria. The results shows that the root mean square error (RMSE) for the Okumura-Hata, COST 231-Hata, Ericsson model, Lee, Stanford University Interim (SUI), ECC-33, and modified models are 14.33, 9.73, 25.79, 48.4, 33.76, and 8.31 dB respectively. Additionally, the Ericsson model provided 0.498 dB, the COST 231-Hata recorded 0.733 dB, and the modified model provided 0.453 dB for mean absolute percentage error (MAPE). Therefore, the improved model produces the best results, consequently, be deployed to approximately predict path loss for mobile radio coverage in Owerri, Nigeria.
Performance evaluation of botnet detection using machine learning techniques Padhiar, Sneha; Patel, Ritesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6827-6835

Abstract

Cybersecurity is seriously threatened by Botnets, which are controlled networks of compromised computers. The evolving techniques used by botnet operators make it difficult for traditional methods of botnet identification to stay up. Machine learning has become increasingly effective in recent years as a means of identifying and reducing these hazards. The CTU-13 dataset, a frequently used dataset in the field of cybersecurity, is used in this study to offer a machine learning-based method for botnet detection. The suggested methodology makes use of the CTU-13, which is made up of actual network traffic data that was recorded in a network environment that had been attacked by a botnet. The dataset is used to train a variety of machine learning algorithms to categorize network traffic as botnet-related/benign, including decision tree, regression model, naïve Bayes, and neural network model. We employ a number of criteria, such as accuracy, precision, and sensitivity, to measure how well each model performs in categorizing both known and unidentified botnet traffic patterns. Results from experiments show how well the machine learning based approach detects botnet with accuracy. It is potential for use in actual world is demonstrated by the suggested system’s high detection rates and low false positive rates.
Optimal coding unit decision for early termination in high efficiency video coding using enhanced whale optimization algorithm Krishnegowda, Suhas Shankarnahalli; Periapandi, Hosanna Princye
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6378-6387

Abstract

Video compression is an emerging research topic in the field of block based video encoders. Due to the growth of video coding technologies, high efficiency video coding (HEVC) delivers superior coding performance. With the increased encoding complexity, the HEVC enhances the rate-distortion (RD) performance. In the video compression, the out-sized coding units (CUs) have higher encoding complexity. Therefore, the computational encoding cost and complexity remain vital concerns, which need to be considered as an optimization task. In this manuscript, an enhanced whale optimization algorithm (EWOA) is implemented to reduce the computational time and complexity of the HEVC. In the EWOA, a cosine function is incorporated with the controlling parameter A and two correlation factors are included in the WOA for controlling the position of whales and regulating the movement of search mechanism during the optimization and search processes. The bit streams in the Luma-coding tree block are selected using EWOA that defines the CU neighbors and is used in the HEVC. The results indicate that the EWOA achieves best bit rate (BR), time saving, and peak signal to noise ratio (PSNR). The EWOA showed 0.006-0.012 dB higher PSNR than the existing models in the real-time videos.
Virtual environment for assistant mobile robot Herrera, Jorge Jaramillo; Jimenez-Moreno, Robinson; Martinez Baquero, Javier Eduardo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6174-6184

Abstract

This paper shows the development of a virtual environment for a mobile robotic system with the ability to recognize basic voice commands, which are oriented to the recognition of a valid command of bring or take an object from a specific destination in residential spaces. The recognition of the voice command and the objects with which the robot will assist the user, is performed by a machine vision system based on the capture of the scene, where the robot is located. In relation to each captured image, a convolutional network based on regions is used with transfer learning, to identify the objects of interest. For human-robot interaction through voice, a convolutional neural network (CNN) of 6 convolution layers is used, oriented to recognize the commands to carry and bring specific objects inside the residential virtual environment. The use of convolutional networks allowed the adequate recognition of words and objects, which by means of the associated robot kinematics give rise to the execution of carry/bring commands, obtaining a navigation algorithm that operates successfully, where the manipulation of the objects exceeded 90%. Allowing the robot to move in the virtual environment even with the obstruction of objects in the navigation path.<
An efficient reconfigurable geographic routing congestion control algorithm for wireless sensor networks Pandith, Mamatha M.; Ramaswamy, Nataraj Kanathur; Srikantaswamy, Mallikarjunaswamy; Ramaswamy, Rekha Kanathur
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6388-6398

Abstract

In recent times, huge data is transferred from source to destination through multi path in wireless sensor networks (WSNs). Due to this more congestion occurs in the communication path. Hence, original data will be lost and delay problems arise at receiver end. The above-mentioned drawbacks can be overcome by the proposed efficient reconfigurable geographic routing congestion control (RgRCC) algorithm for wireless sensor networks. the proposed algorithm efficiently finds the node’s congestion status with the help queue length’s threshold level along with its change rate. Apart from this, the proposed algorithm re-routes the communication path to avoid congestion and enhances the strength of scalability of data communication in WSNs. The proposed algorithm frequently updates the distance between the nodes and by-pass routing holes, common for geographical routing. when the nodes are at the edge of the hole, it will create congestion between the nodes in WSNs. Apart from this, more nodes sink due to congestion. it can be reduced with the help of the proposed RgRCC algorithm. As per the simulation analysis, the proposed work indicates improved performance in comparison to conventional algorithm. By effectively identifying the data congestion in WSNs with high scalability rate as compared to conventional methods
Adversarial attack driven data augmentation for medical images Pervin, Mst. Tasnim; Tao, Linmi; Huq, Aminul
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6285-6292

Abstract

An important stage in medical image analysis is segmentation, which aids in focusing on the required area of an image and speeds up findings. Fortunately, deep learning models have taken over with their high-performing capabilities, making this process simpler. The deep learning model’s reliance on vast data, however, makes it difficult to utilize for medical image analysis due to the scarcity of data samples. Too far, a number of data augmentations techniques have been employed to address the issue of data unavailability. Here, we present a novel method of augmentation that enabled the UNet model to segment the input dataset with about 90% accuracy in just 30 epochs. We describe the us- age of fast gradient sign method (FGSM) as an augmentation tool for adversarial machine learning attack methods. Besides, we have developed the method of Inverse FGSM, which im- proves performance by operating in the opposite way from FGSM adversarial attacks. In comparison to the conventional FGSM methodology, our strategy boosted performance up to 6% to 7% on average. The model became more resilient to hostile attacks because to these two strategies. An innovative implementation of adversarial machine learning and resilience augmentation is revealed by the overall analysis of this study.
Improving the performance of free space optical systems: a space-time orthogonal frequency division modulation approach Ojo, Adedayo Olukayode; Owolabi, Isreal Esan; Onibonoje, Moses Oluwafemi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6435-6442

Abstract

Free space optical (FSO) communication systems are known for high capacity and information security. The overall system performances of FSO systems are however significantly affected by atmospheric turbulence induced fading. This paper, therefore, proposes a technique to mitigate this effect through the introduction of an additional degree of error correction capacity by exploiting the spectral dimension in the coding space. A space-time trellis coded orthogonal frequency division modulation (OFDM) scheme was developed, simulated and evaluated for optical communication through a Gamma-Gamma channel. The evaluation of the coding gain obtained from the simulation results, the mathematical analysis and the truncation error analysis shows that the proposed technique is a promising and viable technique for improving the error correction performance of space-time codes for free space optical communication links.
Harmonic assessment on two photovoltaic inverter modes and mathematical models on low voltage network power quality Penangsang, Ontoseno; Wibowo, Rony Seto; Aryani, Ni Ketut; Prasetyo, Mario Dwi; Arianto, Marcel Nicky; Lutfi, Aulia Amjad
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp5951-5965

Abstract

Power quality is a crucial aspect of designing a large-scale photovoltaic power plant, particularly regarding harmonics caused by inverter switching. This research aimed to analyze harmonics in a system using electrical transient analyzer program (ETAP) Power Station 20.5.0 to uncover the effect of irradiance on the inverters’ power quality running at 85% and 100% power factors. We analyzed both voltage and current total harmonic distortion (THDi and THDv) from the simulation and compared them with the mathematical model. Moreover, we analyzed the effect of changes in irradiance level on harmonics and reactive power penetration, which influenced power losses in transformers and cables. Inverters at 85% power factor experienced an increase in THDi, whereas those at 100% power factor decreased. Inverters with 85% power factor experienced more frequent switching, causing more prominent distortion. The magnitude of THDv increased proportionally with the rise of irradiance level. Inverters at 85% had a higher THDv value because of the excessive reactive power compensation when irradiance rose. Irradiance level had an inverse relationship with system losses since high irradiance levels led to lower losses as less power was required through transmission lines and transformers. Moreover, losses at 85% power factor were higher since the high harmonics caused additional losses.

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